• All the courses

• Course information

• Sequence models: focus on time series (there are others) — stock, weather,...

• At the end, we wanna model sunspot actitivity cycles which is important to NASA and other space agencies.

• Using RNN on time series data.

Sequences and prediction

Time Series

📙 Notebook: introduction to time series. + explaining video. → How to create synthetic time series data + plot them.

• Time series is everywhere: stock prices, weather focasts, historical trends (Moore's law),...
• Univariate TS and Miltivariate TS.
• Type of things can we do with ML over TS:
  • Any thing has a time factor can be analysed using TS.
  • Predicting a forecasting (eg. birth & death in Japan -> predict future for retirement, immigration, impacts...).
  • Imputation: project back into the past.
  • Fill holes in the data.
  • Nomalies detecction (website attacks).
  • Spot patterns (eg. speed recognition).
• Common patterns in TS:

  • Trend: a specific direcion that they're moving in.

    

  • Seasonality: patterns repeat at predictable intervals (eg. active users for a website).

    

  • Combinition of both trend and seasonality.

    

  • Stationary TS

    

  • Autocorrelated TS: a time series is linearly related to a lagged version of itself.. There is no trend, no seasonality.

    

  • Multiple auto correlation.

    

  • May be trend + seasonality + autorrelation + noise.

    

  • Non-stationary TS In this case, we base just on the later data to predict the future (not on the whole data).

    

Train / Validation / Test

• Fixed partitioning (this course focuses on) = splitting TS data into training period, validation period and test period.

  • If TS is seasonal, we want each period contains the whole number of seasons.

  

• We can split + train + test to get a model and then re-train with the data containing also the test period so that the model is optimized! In that case, the test set comes from the future.

  

• Roll-forward partitioning: we start with a short training period and we gradually increase it (1 day at a time or 1 week at a time). At each iteration, we train the model on training period, use it to focast the following day/week in the validation period. = Fixed partitioning in a number of times!

  

Metrics

For evaluating models:

errors = forecasts - actual

# Mean squared error (square to get rid of negative values)
# Eg. Used if large errors are potentially dangerous
mse = np.square(errors).mean()
# Get back to the same scale to error
rmse = np.sqrt(mse)

# Mean absolute error (his favorite)
# this doesn't penalize large errs as much as mse does,
# used if loss is proportional to the size of err
mae = np.abs(errors).mean()

# Mean abs percentage err
# idea of the size of err compared to the values
mape = np.abs(errors / x_valid).mean()

# MAE with TF
keras.metrics.mean_absolute_error(x_valid, naive_forecast).numpy()